As the first step towards understanding backdoor attacks in FedGAN for medical image synthesis, our work brings insight into building a robust and trustworthy model to advance medical research with synthetic data. Furthermore, we hope to highlight that FedDetect involves only lightweight improvement on the server aggregation step. This makes FedDetect flexible to integrate into different GAN-based federated generative models. Our future work includes scaling up the FL system with more clients, generalizing FedDetect to other deep generative models, e.g., Diffusion models [1]}, and considering other variants of backdoor attacks, e.g., frequency-injection based attack [2]}.
